PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Simple and SimplerSVM
S V N Vishwanathan, Alex Smola and Nicol Schraudolph
In: NIPS Workshop on Large Scale Kernel Machines, 05 - 11 December 2005, Vancouver, Canada.

Abstract

We present a fast iterative support vector training algorithm for the quadratic hard margin formulation. Our algorithm works by incrementally changing a candidate support vector set using a locally greedy approach, until the supporting hyperplane is found within a finite number of iterations. It is derived by a simple (yet computationally crucial) modification of the incremental SVM training algorithms of Cauwenberghs and Poggio which allows us to perform update operations very efficiently, in particular when the kernel matrix is rank-degenerate. Constant-time methods for initialization of the algorithm and experimental evidence for the speed of the proposed algorithm, when compared to methods such as Sequential Minimal Optimization and the Nearest Point Algorithm are given. We also indicate methods to extend our algorithm to the linear soft margin loss formulation. We present results on a variety of real life datasets to validate our claims.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2047
Deposited By:S V N Vishwanathan
Deposited On:16 January 2006